Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Uprooting and Rerooting Higher-Order Graphical Models
Authors: Mark Rowland, Adrian Weller
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate empirically that rerooting can significantly improve accuracy of methods of inference for higher-order models at negligible computational cost. |
| Researcher Affiliation | Academia | Mark Rowland University of Cambridge EMAIL Adrian Weller University of Cambridge and Alan Turing Institute EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper mentions using a third-party library ('All methods were implemented using lib DAI [8]') but does not provide a link or explicit statement about releasing its own source code for the described methodology. |
| Open Datasets | No | The paper describes generating synthetic models for experiments ('complete hypergraphs (with 8 variables) and toroidal grid models (5 x 5 variables). Potentials up to order 4 were selected randomly'), but does not refer to or provide access to a publicly available or open dataset in the traditional sense. |
| Dataset Splits | No | The paper does not specify training, validation, or test dataset splits, as the experiments involve running inference on randomly generated model instances rather than splitting a fixed dataset. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used for running the experiments (e.g., specific CPU/GPU models, cloud instances). |
| Software Dependencies | No | The paper states 'All methods were implemented using lib DAI [8]', but it does not provide a specific version number for lib DAI or any other ancillary software dependencies, which is required for reproducibility. |
| Experiment Setup | No | The paper describes the types of models and inference methods used (e.g., 'double loop method... which relates to generalized belief propagation, 24) and MAP inference (using loopy belief propagation, LBP [9])'), but it does not provide specific numerical hyperparameters (e.g., learning rates, batch sizes, epochs) for these methods or the heuristics. |